Biology subjects

Mostafavi, H.

Publications and source records attributed to Mostafavi, H..

15 recordsLinked to original sources

bioRxiv2026

Expanding Reverse Genetics of Positive-Strand RNA Viruses: Optimised Rescue Platforms and Construction of a Novel Fluorescent Reporter Nidovirus

Reverse genetics systems are crucial for facilitating the precise manipulation of viruses across a wide spectrum of translational and fundamental research pipelines. Here, we compared Circular polymerase extension reaction (CPER), Gibson assembly, and infectious subgenomic amplicons (ISA) for bacteria-free recovery of a positive sense RNA virus. Through optimisation of CPER, we demonstrated accelerated virus recovery and enhanced viral yields. We further investigated strategies to improve rescue efficiency across diverse positive-sense RNA virus families through incorporation of alternative promoters and non-coding elements. To evaluate the performance of the Aedes aegypti polyubiquitin promoter (AePUb) in tandem with a hammerhead ribozyme (HH Rbz) and a polymerase pause site for virus recovery in insect cells, we constructed a new fluorescent reporter genome using a 20 kb insect-specific mesonivirus. In vitro recovery by CPER of the mesonivirus was achievable in 1 day when using AePUb with HH Rbz, in comparison to a four-day recovery when using the minimal OpIE2-CA promoter. These elements were additionally assessed for rescue of the orthoflaviviruses, Binjari virus (BinJV) and dengue virus 2 (DENV-2), in insect cells (using AePUb); or in mammalian cells (using the CMV promoter) and for launch of DENV2 and SARS-CoV-2. Both BinJV and DENV-2 demonstrated improved rescue with the AePUb promoter and HH Rbz. However, the addition of the HH Rbz and the polymerase pause site to the CMV linker fragment showed no significant differences to the standard CMV promoter systems for both DENV-2 and SARS-CoV-2, highlighting the context-specific benefits of their implementation. In summary, we demonstrated that a potent constitutive promoter system and a hammerhead ribozyme significantly enhance the efficiency of positive-sense RNA virus rescue using CPER.

molecular biology↗

bioRxiv2026

IL-1β and TNF drive endothelial dysfunction and coagulopathy in acute COVID-19.

Vascular dysfunction and coagulopathy are hallmarks of severe COVID-19. How SARS-CoV-2 infection drives endothelial dysfunction, despite the virus not infecting or replicating in endothelial cells, remains controversial. Here, we used an in vitro co-culture model of the human pulmonary epithelial-endothelial cell barrier to investigate which inflammatory mediators drive endothelial dysfunction during SARS-CoV-2 infection. SARS-CoV-2 infection of primary human bronchial epithelial cells increased adjacent endothelial cell expression of the leukocyte adhesion marker ICAM-1, disrupted endothelial VE-cadherin junctions, promoted endothelial cell death, and promoted platelet adherence to gaps in the endothelial monolayers. Dexamethasone treatment rescued these dysregulated endothelial phenotypes in infected co-cultures, confirming that inflammatory signalling was the primary driver of SARS-CoV-2-induced endothelial dysfunction. Specifically, epithelial-derived TNF and IL-1{beta} promoted endothelial dysfunction, as inhibition of TNF or IL-1R signalling blocked SARS-CoV-2-induced endothelial dysfunction in co-cultures. SARS-CoV-2-infected wild-type mice, but not TNF, IL-1{beta}, or TNF/IL-1{beta}- deficient mice, displayed increased endothelial ICAM-1 expression, while an anti-IL-1{beta} monoclonal antibody prevented SARS-CoV-2-induced ICAM-1 expression and fibrin deposition in aged K18-ACE2 mice. Our data indicate that TNF and IL-1{beta} are the specific cytokines that drive multiple aspects of endothelial dysfunction during acute SARS-CoV-2 infection, and that inhibiting their signalling pathways may provide therapeutic benefit in preventing vascular complications of COVID-19.

cell biology↗

bioRxiv2025

Omicron evolution drives increased ciliated cell tropism and dysfunction in nasal epithelia.

SARS-CoV-2 Omicron variants demonstrate greater replication efficiency in the upper respiratory tract than both ancestral virus and Delta variant, potentially due to stronger interactions with motile cilia in human nasal epithelial cells. Despite the emergence of numerous Omicron variants, there has been no comprehensive investigation comparing their interactions with ciliated cells. Here, we use differentiated primary human nasal epithelial cell (NEC) cultures to compare an ancestral isolate (QLD02) to Omicron variants BA.1, BA.5 and XBB. Transcriptomic analysis revealed that BA.5 and XBB infection, unlike BA.1 and QLD02, drives a distinct host response, defined by the profound downregulation of genes essential for cilium assembly and function at 48 hours post-infection. The BA.5/XBB signature was also characterized by the strong upregulation of pro-apoptotic and inflammatory pathways. Immunofluorescence of nucleocapsid-positive cells in NECs showed a significant 13-fold increase for BA.5 relative to QLD02. Additionally, BA.1 and BA.5 exhibited greater tropism for ciliated cells (7.6-fold for BA.1, 9-fold for BA.5) than QLD02. BA.5 and BA.1 infection were associated with elevated apoptosis in both infected and bystander cells, however, the total number of ciliated cells remained unchanged. Consistent with this, BA.5 infection significantly reduced DNAH5 outer dynein arm protein in the cilia, indicating dysfunction of cilia motility rather than overt cilia loss. Overall, we demonstrate a stepwise evolution at the nasal barrier: while enhanced ciliated cell tropism and elevated apoptosis are conserved features of Omicron infection, ciliary dysfunction, evident at both the transcriptional and protein level, is a more pronounced phenotype emerging with BA.5 and preserved in the XBB variant. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/689397v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@11b1b60org.highwire.dtl.DTLVardef@a25e0borg.highwire.dtl.DTLVardef@1cc3904org.highwire.dtl.DTLVardef@b35447_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

A Litmus Test for Confounding in Polygenic Scores

Polygenic scores (PGSs) are being rapidly adopted for trait prediction in the clinic and beyond. PGSs are often thought of as capturing the direct genetic effect of one's genotype on one's phenotype. However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including stratification, assortative mating, and dynastic effects ("SAD effects"). Our interpretation and application of PGSs may hinge on the relative influence of SAD effects, since they may often be environmentally or culturally mediated. We developed a method to measure these influences, Partitioning Genetic Scores Using Siblings (PGSUS, pron. "Pegasus"). PGSUS leverages a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS--which is largely immune to SAD effects--to partition variance in a PGS (in a given sample) into components due to direct effects, SAD effects, and their covariance. Using PGSUS, we found that in many cases direct genetic effects contribute relatively little to PGS variation--most pronouncedly so in PGSs for social or behavioral traits, such as educational attainment or neuroticism. PGSUS further breaks down variance components by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects. In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is "isotropic" with respect to axes of ancestry. Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height as well as in multiple PGSs constructed using the UK Biobank. We show that a given PGS can suffer from stratification along a major axis of ancestry in one sample but not in another (for example, in comparisons of prediction in samples from contemporary vs. ancient DNA samples). We further show that when axes of stratification are shared between GWAS and prediction samples, stratification can both aid and impede phenotypic prediction accuracy. In summary, PGSUS offers advances in interpretation towards more informed application of polygenic scores.

genomics↗

bioRxiv2024

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance -- how much a gene quantitatively affects a trait; and 2) trait specificity -- a genes importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, while burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

genomics↗

bioRxiv2024

Conditional frequency spectra as a tool for studying selection on complex traits in biobanks

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of frequency and effect size -- but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. To account for GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insight into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.

genetics↗

bioRxiv2023

Scaling the Discrete-time Wright Fisher model to biobank-scale datasets

The Discrete-Time Wright Fisher (DTWF) model and its large population diffusion limit are central to population genetics. These models describe the forward-in-time evolution of the frequency of an allele in a population and can include the fundamental forces of genetic drift, mutation, and selection. Computing like-lihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large sample sizes or in the presence of strong selection. Unfortunately, existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here we present an algorithm that approximates the DTWF model with provably bounded error and runs in time linear in the size of the population. Our approach relies on two key observations about Binomial distributions. The first is that Binomial distributions are approximately sparse. The second is that Binomial distributions with similar success probabilities are extremely close as distributions, allowing us to approximate the DTWF Markov transition matrix as a very low rank matrix. Together, these observations enable matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the billions, paving the way for rigorous biobank-scale population genetic inference. Finally, we use our results to estimate how increasing sample sizes will improve the estimation of selection coefficients acting on loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects.

evolutionary biology↗

bioRxiv2023

Bayesian estimation of gene constraint from an evolutionary model with gene features

Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ~25% of genes, potentially causing important pathogenic mutations to be over-looked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease, and other phenotypes, especially for short genes. Our new estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve estimation of many gene-level properties, such as rare variant burden or gene expression differences.

genetics↗

bioRxiv2022

Simple scaling laws control the genetic architectures of human complex traits

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 diverse, highly polygenic quantitative traits from the UK Biobank. Notably, we infer similar distributions of selection coefficients across all these traits. This shared distribution implies that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude across traits. When these two scale factors are accounted for, the architectures of all 95 traits are nearly identical.

genetics↗

bioRxiv2022

Limited overlap of eQTLs and GWAS hits due to systematic differences in discovery

Most signals in genome-wide association studies (GWAS) of complex traits point to noncoding genetic variants with putative gene regulatory effects. However, currently identified expression quantitative trait loci (eQTLs) explain only a small fraction of GWAS signals. By analyzing GWAS hits for complex traits in the UK Biobank, and cis-eQTLs from the GTEx consortium, we show that these assays systematically discover different types of genes and variants: eQTLs cluster strongly near transcription start sites, while GWAS hits do not. Genes near GWAS hits are enriched in numerous functional annotations, are under strong selective constraint and have a complex regulatory landscape across different tissue/cell types, while genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally-relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variants, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

genomics↗

bioRxiv2021

Effect sizes of causal variants for gene expression and complex traits differ between populations

Despite the growing number of genome-wide association studies (GWAS), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWAS are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

genetics↗

bioRxiv2019

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

genetics↗

bioRxiv2018

Measuring intolerance to mutation in human genetics

In numerous applications, from working with animal models to mapping the genetic basis of human disease susceptibility, it is useful to know whether a single disrupting mutation in a gene is likely to be deleterious1-4. With this goal in mind, a number of measures have been developed to identify genes in which protein-truncating variants (PTVs), or other types of mutations, are absent or kept at very low frequency in large population samples--genes that appear \"intolerant to mutation\"3,5-9. One measure in particular, pLI, has been widely adopted7. By contrasting the observed versus expected number of PTVs, it aims to classify genes into three categories, labelled \"null\", \"recessive\" and \"haploinsufficient\"7. Such population genetic approaches can be useful in many applications. As we clarify, however, these measures reflect the strength of selection acting on heterozygotes, and not dominance for fitness or haploinsufficiency for other phenotypes.

genetics↗

bioRxiv2018

Reduced signal for polygenic adaptation of height in UK Biobank

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect-size estimates for height are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

evolutionary biology↗

bioRxiv2016

Identifying genetic variants that affect viability in large cohorts

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we find only a few common variants with large effects on age-specific mortality: tagging the APOE {varepsilon}4 allele and near CHRNA3. These results suggest that when large, even late onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence one of 42 traits, we detect a number of strong signals. In participants of the UK Biobank study of British ancestry, we find that variants that delay puberty timing are enriched in longer-lived parents (P~6x10-6 for fathers and P~2x10-3 for mothers), consistent with epidemiological studies. Similarly, in mothers, variants associated with later age at first birth are associated with a longer lifespan (P~1x10-3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease, body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. Moreover, we see marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of coronary artery disease and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical datasets can be used to learn about selection effects in contemporary humans.

evolutionary biology↗